Most of our analyses are applicable to general fitness and mutation (i.e., adaptive) landscapes (Appendix , ). However, we characterize in detail the features of artificial selection to direct evolution on high dimensional quadratic adaptive landscapes (\(F= -{\bf x}^\top \cdot C \cdot {\bf x}\) ), in which \(C\)  is the adaptive pressure and \({\bf x}\)  is the shifted phenotype vector centered around the optimum under natural selection that the population approaches in stationary state [1]}; see Appendix . In addition, we assume a quadratic form for the cost function throughout the evolutionary process, \(V ({\bf x},t) = \frac{1}{2}({\bf x}_t-{\bf x}^*)^\top G ({\bf x}_t-{\bf x}^*)\)  and also at the end point \( Q ({\bf x},{t_f}) = \frac{1}{2} ({\bf x}_{t_f}-{\bf x}^*)^\top \tilde{G} ({\bf x}_{t_f}-{\bf x}^*)\) .
